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abcdiscrete

Python software for black-box optimization for discrete data. Based on ideas from Approximate Bayesian Computation and Differential Evolution.

bayesde

Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"

bayesiancnn-dna

This repository contains python code for identifying DNA motifs (short DNA sequences) using Bayesian CNNs combined with an attention mechanism

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

deeprl

Python code for Deep Q-Learning learning. The algorithm was investigated as an optimal solution for three tasks: (1) obstacleavoidance, (2) foraging and (3) predator-prey behaviour.

diffcosim

By introducing a differentiable contact model, DiffCoSim extends the applicability of Lagrangian/Hamiltonian-inspired neural networks to enable learning of hybrid dynamics.

dl2_tutorial2

Material for the Deep Learning 2 Tutorial

fnp

Implementation of the Functional Neural Process models

fourier_neural_operator

Use Fourier transform to learn operators in differential equations.

fourierflow

[ML4PS @ NeurIPS 2021] Factorized Fourier Neural Operators

google-research

Google Research

hypernetworks

PyTorch implementation of HyperNetworks (Ha et al., ICLR 2017) for ResNet (Residual Networks)

idsprites

Easily generate continual learning benchmarks.

ilzeamandaa.github.io

A beautiful, simple, clean, and responsive Jekyll theme for academics

inv_latent_ode

Code for "Latent ODEs for Irregularly-Sampled Time Series" paper

leads

Learning Dynamical Systems that Generalize Across Environments

lpsda

Repo to the paper "Lie Point Symmetry Data Augmentation for Neural PDE Solvers"

monode

Code base for Modulated Neural ODEs (MoNODEs), a novel framework that sets apart dynamics states from underlying static factors of variation and improves the existing NODE methods.

neural-flows-experiments

Experiments for Neural Flows paper

neural-spdes

neuralwavemachines

Official Implementation of the ICML 2023 paper: "Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks"

ode2vae

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

phiflow

A differentiable PDE solving framework for machine learning

physics-based-deep-learning

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

pytorch-ensembles

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020

scalable-inference-in-sdes

Methods and experiments for assumed density SDE approximations

simdnagen

Python code to generate simulation DNA sequences with relevant motif (encoding DNA) insertion

slotformer

Code release for ICLR 2023 paper: SlotFormer on object-centric dynamics models

srvp

Official implementation of the paper Stochastic Latent Residual Video Prediction

steve_base

Official code for Slot-Transformer for Videos (STEVE)